spring23_ML_Classification

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Jan 9, 2024

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CSE 4/574 Machine Learning A&C&D Spring 2023 [Bonus] Buffalo Trees Classification Welcome to the second week of the semester! We hope you enjoyed our Meet & Greet Activity and claimed your bonus. We have another activity for you to complete this week. TASK In this challenge, we will be performing classification using the real-world Tree Inventory dataset ( link ). This dataset comprises of all street trees within the City of Buffalo. • The target variable is Common Name • You need to build a classifier, that will predict at least 5 classes, e.g., Maple Norway, Linden …. As part of this task, you do not have to use the whole dataset. • Input: you need to use at least 5 features as input. e.g., you can use all the 16 columns or drop some. • You can use any in-build machine learning tools to do this classification or build something from scratch, e.g., Logistic Regression, SVM, Decision tree. Submission that uses neural network or a deep learning framework will not be used for the bonus points. • Main motivation of this task is to explore various ML tools to solve a real-world task. Suggestions: • Preprocess your dataset, e.g., drop the vacant entries and normalize the dataset. • You can use a combination of various ML tools in one pipeline • Check the accuracy using the following snippet:

CSE 4/574 Machine Learning A&C&D Spring 2023 SUBMISSION • Share the following details as a comment to the piazza post: Target: # classes to predict Input: # of features used Model used: (e.g. logistic regression) Test accuracy: (e.g. 74.59%) • Submit a Jupyter notebook or py file with your code and saved output at UBlearns (UBlearns > Challenge) • Save and submit weights for your trained model. • Each student can make a max of 5 submissions for this challenge. Submission with the highest accuracy will be considered. • Only top 5 submissions from different students with the highest accuracy will be considered towards the bonus points. • In your Jupyter Notebook or py file include all the references used, e.g., sckit-learn library. Evaluation The submission with the highest test accuracy will get a bonus point and the code will be presented as a presentation during one of our OH. Time complexity and overall performance of the model will be considered as well. Due date: Monday, Feb 13 at 11:59 pm Notes: - If the file and/or piazza comment is submitted after the due date, e.g., 00:00am, it will not be considered for the bonus. - Only submissions with a publicly shared accuracy on the piazza post will be considered. - No late days can be applied for bonus-based tasks. - This challenge must be done individually. e.g., submissions from different students with a similar structure or similar setup or same answers will not be considered. This will affect the initial submission and all other similar submissions.

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